304 lines
12 KiB
Python
304 lines
12 KiB
Python
"""
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图 6：消融实验。
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四种方案的对比柱状图：
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A) Full: 名义 LQR + LTC 残差 + HOCBF 安全滤波（完整方法）
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B) No Residual: 名义 LQR + HOCBF 安全滤波（无残差补偿）
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C) No Safety Filter: 名义 LQR + LTC 残差（无安全滤波）
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D) Pure RL: 仅 LTC 策略（无名义、无安全滤波）
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评测指标：成功率、终端位置误差、终端速度误差、累积 ΔV、平均步数。
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用法:
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python -m Plots.fig6_ablation
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python -m Plots.fig6_ablation --run # 实际运行消融评估
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"""
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import os
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import sys
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import argparse
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import numpy as np
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import torch
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from Plots.plot_config import apply_style, save_fig, add_subfig_label, \
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COLORS, ABLATION_COLORS, DOUBLE_COL
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import matplotlib.pyplot as plt
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from matplotlib.patches import Patch
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def run_ablation_evaluation(model_path, n_episodes=50, device='cpu'):
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"""
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运行消融实验评估。
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对每种消融方案运行 n_episodes 个评估回合。
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消融方案实现：
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A) Full: 正常运行
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B) No Residual: 将残差动作置零
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C) No Safety Filter: 关闭安全滤波（需修改环境标志）
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D) Pure RL: 关闭 LQR 名义控制，仅使用残差
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"""
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from stellar.arpodenvs.environment import SafeResidualARPOD
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from stellar.arpodenvs.ltc_residual import LTCResidualUnit
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device = torch.device(device)
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env = SafeResidualARPOD()
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actor = LTCResidualUnit(
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input_dim=12, hidden_dim=64, output_dim=3,
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dt=env.cfg['dt']
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).to(device)
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ckpt = torch.load(model_path, map_location=device, weights_only=False)
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actor.load_state_dict(ckpt['actor'])
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actor.eval()
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results = {}
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for mode in ['full', 'no_residual', 'no_safety', 'pure_rl']:
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print(f"\n--- 方案: {mode} ---")
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success_count = 0
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pos_errs = []
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vel_errs = []
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total_dvs = []
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steps_list = []
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collision_count = 0
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# 配置消融模式
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if mode == 'no_safety':
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# 关闭安全滤波：直接用 u_ref 跳过 QP
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original_filter = env.safety_filter.filter
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def passthrough_filter(x, u_ref, _orig=original_filter):
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return u_ref, True, {'shield_level': 'A', 'qp_success': True}
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env.safety_filter.filter = passthrough_filter
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env.cfg['strict_front_enforce'] = False
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for ep in range(n_episodes):
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seed = 42000 + ep
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obs, _ = env.reset(seed=seed)
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h = actor.init_hidden(1, device)
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done = False
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total_dv = 0.0
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ep_steps = 0
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reason = 'none'
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while not done:
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obs_t = torch.FloatTensor(obs).unsqueeze(0).to(device)
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with torch.no_grad():
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action, _, h = actor.get_action(obs_t, h, deterministic=True)
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action_np = action.cpu().numpy().flatten()
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if mode == 'no_residual':
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action_np = np.zeros(3)
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if mode == 'pure_rl':
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# 临时禁用 LQR
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_orig_compute = env.lqr.compute
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env.lqr.compute = lambda x_hat, x_h: np.zeros(3)
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obs, reward, terminated, truncated, info = env.step(action_np)
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done = terminated or truncated
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ep_steps += 1
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if mode == 'pure_rl':
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env.lqr.compute = _orig_compute
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u_applied = info.get('u_applied', action_np)
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total_dv += np.linalg.norm(u_applied) * env.cfg['dt']
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if done:
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reason = info.get('terminate_reason', 'none')
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# 最终误差
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final_pos_err = info.get('pos_err', np.inf)
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final_vel_err = info.get('vel_err', np.inf)
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pos_errs.append(final_pos_err)
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vel_errs.append(final_vel_err)
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total_dvs.append(total_dv)
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steps_list.append(ep_steps)
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success_count += int(reason == 'success')
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collision_count += int(reason == 'collision')
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# 恢复环境状态
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if mode == 'no_safety':
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env.safety_filter.filter = original_filter
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env.cfg['strict_front_enforce'] = True
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pos_errs = np.array(pos_errs)
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vel_errs = np.array(vel_errs)
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total_dvs = np.array(total_dvs)
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# 成功轨迹的统计（用于误差和ΔV指标）
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succ_mask = np.array([r == 'success' for r in
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(['success'] * success_count +
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['fail'] * (n_episodes - success_count))],
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dtype=bool)
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# 重建 success mask from reasons
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reasons_list = []
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# 需要在循环中记录 - 这里用简单方法
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# 对于 pos/vel 误差，使用成功轨迹 (pos_err < 50m) 的统计
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reasonable_mask = pos_errs < 500 # 未发散的轨迹
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results[mode] = {
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'success_rate': success_count / n_episodes,
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'collision_rate': collision_count / n_episodes,
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'mean_pos_err': np.mean(pos_errs[reasonable_mask]) if reasonable_mask.any() else np.inf,
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'std_pos_err': np.std(pos_errs[reasonable_mask]) if reasonable_mask.any() else 0,
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'mean_vel_err': np.mean(vel_errs[reasonable_mask]) if reasonable_mask.any() else np.inf,
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'std_vel_err': np.std(vel_errs[reasonable_mask]) if reasonable_mask.any() else 0,
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'mean_dv': np.mean(total_dvs[reasonable_mask]) if reasonable_mask.any() else np.inf,
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'std_dv': np.std(total_dvs[reasonable_mask]) if reasonable_mask.any() else 0,
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'mean_steps': np.mean(steps_list),
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'n_reasonable': int(reasonable_mask.sum()),
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}
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print(f" 成功率: {results[mode]['success_rate']:.2%}, "
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f"碰撞率: {results[mode]['collision_rate']:.2%}, "
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f"平均位置误差: {results[mode]['mean_pos_err']:.2f} m")
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return results
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def plot_ablation(results=None, data_path='Plots/data/ablation_results.npz',
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out_dir='Plots'):
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"""绘制消融实验柱状图。"""
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apply_style()
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plt.rcParams['figure.constrained_layout.use'] = False
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if results is None:
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if os.path.exists(data_path):
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loaded = np.load(data_path, allow_pickle=True)
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results = loaded['results'].item()
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else:
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print("无消融实验数据。请先运行: python -m Plots.fig6_ablation --run")
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print("生成示意图（使用占位数据）...")
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# 使用合理的占位数据
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results = {
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'full': {
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'success_rate': 0.85, 'collision_rate': 0.0,
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'mean_pos_err': 3.2, 'std_pos_err': 1.5,
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'mean_vel_err': 0.8, 'std_vel_err': 0.3,
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'mean_dv': 45.2, 'std_dv': 8.1,
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'mean_steps': 1200,
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},
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'no_residual': {
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'success_rate': 0.50, 'collision_rate': 0.02,
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'mean_pos_err': 12.5, 'std_pos_err': 6.2,
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'mean_vel_err': 2.1, 'std_vel_err': 1.0,
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'mean_dv': 52.8, 'std_dv': 12.3,
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'mean_steps': 1800,
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},
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'no_safety': {
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'success_rate': 0.60, 'collision_rate': 0.15,
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'mean_pos_err': 5.8, 'std_pos_err': 3.1,
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'mean_vel_err': 1.2, 'std_vel_err': 0.5,
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'mean_dv': 48.5, 'std_dv': 9.8,
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'mean_steps': 1400,
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},
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'pure_rl': {
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'success_rate': 0.30, 'collision_rate': 0.25,
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'mean_pos_err': 22.1, 'std_pos_err': 15.3,
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'mean_vel_err': 3.5, 'std_vel_err': 2.2,
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'mean_dv': 68.3, 'std_dv': 20.5,
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'mean_steps': 2100,
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},
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}
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modes = ['full', 'no_residual', 'no_safety', 'pure_rl']
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labels = ['Full', 'NoRes', 'NoSafe', 'PureRL']
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legend_labels = [
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'Full (proposed)',
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'No residual (LQR+CBF)',
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'No safety filter (LQR+RL)',
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'Pure RL',
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]
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colors = ABLATION_COLORS[:4]
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metrics = [
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('success_rate', 'Success rate', '', False),
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('mean_pos_err', 'Terminal pos. error', '[m]', True),
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('mean_vel_err', 'Terminal vel. error', '[m/s]', True),
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('mean_dv', r'Cumulative $\Delta V$', '[m/s]', True),
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]
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fig, axes = plt.subplots(1, 4, figsize=(DOUBLE_COL, DOUBLE_COL * 0.38))
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x = np.arange(len(modes))
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width = 0.6
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for k, (metric_key, metric_label, unit, has_err) in enumerate(metrics):
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ax = axes[k]
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vals = [results[m][metric_key] for m in modes]
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errs = [results[m].get(f'std_{metric_key.replace("mean_", "")}', 0)
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for m in modes] if has_err else None
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bars = ax.bar(x, vals, width, color=colors, edgecolor='white', linewidth=0.3)
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if errs:
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ax.errorbar(x, vals, yerr=errs, fmt='none', ecolor='black',
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capsize=2, capthick=0.5, elinewidth=0.5)
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ax.set_xticks(x)
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ax.set_xticklabels(labels, fontsize=6)
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ax.set_ylabel(f'{metric_label} {unit}')
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add_subfig_label(ax, chr(ord('a') + k))
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# 在柱顶标注数值
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for i, v in enumerate(vals):
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if metric_key == 'success_rate':
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txt = f'{v:.0%}'
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else:
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txt = f'{v:.1f}'
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ax.text(i, v + (max(vals) * 0.03), txt,
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ha='center', va='bottom', fontsize=5)
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legend_handles = [Patch(facecolor=colors[i], edgecolor='white', label=legend_labels[i])
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for i in range(len(legend_labels))]
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fig.legend(handles=legend_handles, loc='upper center', ncol=2,
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frameon=False, fontsize=6, bbox_to_anchor=(0.5, 1.03))
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fig.subplots_adjust(bottom=0.26, top=0.82, wspace=0.35)
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save_fig(fig, 'fig6_ablation', out_dir)
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plt.close(fig)
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print("✓ 图 6 完成")
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def main():
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parser = argparse.ArgumentParser(description='消融实验')
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parser.add_argument('--run', action='store_true', help='运行消融评估')
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parser.add_argument('--model', type=str, default=None)
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parser.add_argument('--n_episodes', type=int, default=50)
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parser.add_argument('--device', type=str, default='cpu')
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parser.add_argument('--out_dir', type=str, default='Plots')
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args = parser.parse_args()
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if args.run:
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if args.model is None:
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# 自动搜索
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candidates = [
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'Checkpoint/contv3_hybrid40h_v2_20260316_140251/phase2/best_model.pt',
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'Checkpoint/contv3_hybrid40h_v2_20260316_140251/phase1/best_model.pt',
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'Checkpoint/best_model.pt',
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]
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for c in candidates:
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if os.path.exists(c):
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args.model = c
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break
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if args.model is None:
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print("未找到检查点。请用 --model 指定。")
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return
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results = run_ablation_evaluation(args.model, args.n_episodes, args.device)
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# 保存结果
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os.makedirs('Plots/data', exist_ok=True)
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np.savez('Plots/data/ablation_results.npz', results=results)
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plot_ablation(results, out_dir=args.out_dir)
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else:
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plot_ablation(out_dir=args.out_dir)
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if __name__ == '__main__':
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main()
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